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title
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Learning Implicit Fields from Anchored Radial Observations.
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author
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2026-02-27 05:10:03
raw text
ARO-Net ARO-Net: Learning Implicit Fields from Anchored Radial Observations Yizhi Wang* 1,2 , Zeyu Huang* 1 , Ariel Shamir 3 , Hui Huang 1 , Hao Zhang 2 , Ruizhen Hu 1 , 1 Shenzhen University, 2 Simon Fraser University 3 Reichman University Paper arXiv Video Code ARO is a new representation for shapes based on radial observations from a set of viewpoints. ARO-Net encodes contextual information from anchors, which is different against most existing methods (such as Points2Surf and ConvONet ) that encode neighboring information around query point, yielding better reconstruction (see the holes) on sparse input and generalizability on unseen categories. Abstract We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3D shapes that is category-agnostic and generalizable amid significant shape variations. The main idea behind our work is to reason...
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